Since 2001, the number of financial statement line items forecasted by analysts and managers that I/B/E/S and FactSet capture in their data feeds has soared. Using this new data, we find that 13 item surprises—11 income statement and 2 cash flow statement analyst and management guidance surprises—reliably explain firms’ signed earnings announcement returns.
We propose a general GARCH framework that allows one to predict volatility using returns sampled at a higher frequency than the prediction horizon.
Postdoctoral scholars may be economic complements or substitutes for faculty, doctoral research assistants and capital in the production of university life science research. Using data on 120 US universities, we present two cross-sectional (1993 and 2006) descriptive econometric models. Results suggest that postdocs serve primarily as complements to other labour inputs and capital.
Innovative data sources offer new ways of studying spatial and temporal industrial and regional development. Our approach is to study the development of an entrepreneurial regional economy through a comprehensive analysis of its constituent firms and institutions over time.
We develop Granger causality tests that apply directly to data sampled at different frequencies. We show that taking advantage of mixed frequency data allows us to better recover causal relationships when compared to the conventional common low frequency approach.
We present a survey design that generalizes static conjoint experiments to elicit inter-temporal adoption decisions for durable goods. We show that consumers’ utility and discount functions in a dynamic discrete choice model are jointly identified using data generated by this specific design. In contrast, based on revealed preference data, the utility and discount functions are generally not jointly identified even if consumers’ expectations are known.
Inspired by a data set from the Chinese retailer JD.com, we study the click and purchase behavior of customers in an online retail setting by employing a structural estimation approach.
Hasbrouck (2018) takes advantage of the fact that U.S. equity market data are timestamped to nanosecond precision, and explores models of price dynamics at resolutions sufficient to capture the reactions of the fastest agents. The paper therefore addresses the econometric analysis of multivariate time series models at sub-millisecond frequencies and relies on long distributed lag models to alleviate the computational complexity while still taking advantage of the inherent sparsity of price transitions.
The autonomous car began as an opportunity that required breaking all kinds of limits: engineering, navigation, adjusting to traffic conditions, distinguishing objects, predicting what those objects might do, reacting in time, calculating quickly and juggling a vast number of ever-changing variables. The developers used more and more computer power to address these needs. But the initial bounding limit turned out to be very fundamental; rule-based computers don’t have pattern power.
The COVID-19 pandemic has generated a significant shift in how and where we work, play and live. In this Kenan Insight, we explore which changes will be temporary and which are here to stay.
Although store brands (SBs) are becoming increasingly important across the world, their success varies dramatically across consumer packaged goods categories and countries. The purpose of this paper is to provide insight into how such differences in SB success originate.
The emerging theory-based view depicts entrepreneurs as sophisticated thinkers who form, update, and act on rich causal theories. In support of this view, recent empirical work has demonstrated both (a) the value of theories as well as (b) the importance of experimentation for testing and refining theories. Yet, the process by which entrepreneurs initially form these theories remains largely unobserved.
We study the role of information in asset pricing models with long-run cash flow risk. When investors can distinguish short- from long-run consumption risks (full information), the model generates a sizable equity risk premium only if the equity term structure slopes up, contrary to the data.
How will the hurricanes affect economic data for October? Kenan Institute Research Fellow Greg Brown will look at the data during the institute’s monthly virtual briefing at 9 AM EDT Friday, November 1.
Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work.
As the historic 2020 U.S. presidential election draws nearer, voters are taking stock of the impact the COVID-19 pandemic has had on their lives and livelihoods, and demanding that policymakers present their plans for economic recovery. In this Kenan Insight, we look at the major forces reshaping the U.S. economy and offer suggestions for forging an intentional and equitable path forward.